Time of Flight (ToF) depth image processing methods are disclosed for resolving corruption of ToF depth images. In ToF depth imaging, the light can travel paths of different lengths before returning to the pixel. Thus, the light hitting the pixel can have travelled different distances, and the distance obtained from an estimation procedure assuming a single distance may be spurious. Systems and methods are disclosed for including a time-of-flight imager and processor which resolves the multiple paths, and outputs the multiple depths at each pixel.
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2. The image processing system of claim 1, wherein the processor is further configured to process the first set of measurements to determine a second set of measurements, wherein the second set of measurements is smaller than the first set of measurements.
The invention relates to an image processing system designed to reduce the size of measurement data derived from images. The system addresses the challenge of handling large datasets generated during image analysis, which can be computationally intensive and resource-consuming. By processing an initial set of measurements to produce a smaller, condensed set, the system improves efficiency in data storage, transmission, and further analysis. The system includes a processor that receives a first set of measurements obtained from image analysis. These measurements may include pixel values, feature descriptors, or other quantitative data extracted from the image. The processor then processes this first set to generate a second set of measurements, where the second set is smaller in size. This reduction can be achieved through techniques such as dimensionality reduction, data compression, or feature selection, ensuring that the essential information is retained while minimizing data volume. The reduced dataset can then be used for subsequent tasks like machine learning, pattern recognition, or real-time decision-making, with improved performance and lower computational overhead. The system is particularly useful in applications where image data is abundant, such as medical imaging, surveillance, or autonomous systems, where efficient data handling is critical.
3. The image processing system of claim 2, wherein the processor is further configured to perform dimensionality reduction on the first set of measurements to generate the second set of measurements.
The image processing system operates in the domain of data analysis and visualization, specifically addressing the challenge of efficiently processing high-dimensional image data to extract meaningful features while reducing computational complexity. The system includes a processor configured to analyze image data by capturing a first set of measurements from the image, which may represent raw pixel values, texture features, or other image characteristics. The processor then applies dimensionality reduction techniques to this first set of measurements to generate a second set of measurements. This reduction process condenses the high-dimensional data into a lower-dimensional representation while preserving essential information, enabling faster processing, improved visualization, and more efficient storage. The dimensionality reduction may involve methods such as principal component analysis (PCA), linear discriminant analysis (LDA), or other mathematical transformations that project the data into a subspace with fewer dimensions. By reducing the data's dimensionality, the system enhances computational efficiency and facilitates downstream tasks like classification, clustering, or anomaly detection. The system is particularly useful in applications where large-scale image datasets must be processed in real-time or near-real-time, such as medical imaging, surveillance, or autonomous vehicle systems. The reduced-dimensional measurements can then be used for further analysis, visualization, or machine learning tasks, ensuring that the system remains scalable and adaptable to various imaging applications.
4. The image processing system of claim 2, wherein the processor is further configured to perform discretization on the first set of measurements to generate the second set of measurements.
The image processing system is designed to enhance the accuracy and efficiency of image analysis by transforming raw measurement data into a more manageable format. The system addresses the challenge of processing high-resolution or complex image data, which often contains noise or excessive detail that can hinder analysis. To solve this, the system includes a processor that performs discretization on a first set of measurements to generate a second set of measurements. Discretization involves converting continuous or high-resolution data into discrete values or bins, reducing the data's complexity while preserving essential features. This process simplifies subsequent analysis, improves computational efficiency, and enhances the reliability of image processing tasks. The system may also include additional components, such as sensors or input interfaces, to capture the initial measurements, and output modules to present or store the processed data. By discretizing the measurements, the system ensures that the data is optimized for further processing, such as pattern recognition, object detection, or feature extraction, without losing critical information. This approach is particularly useful in applications like medical imaging, remote sensing, or industrial inspection, where precise and efficient data handling is essential.
5. The image processing system of claim 4, wherein performing the discretization comprises selecting a discrete set of values from the first set of measurements, and wherein the look-up table comprises precomputed solutions for the discrete set of values.
The invention relates to an image processing system designed to optimize computational efficiency in image analysis tasks. The system addresses the problem of high computational costs associated with processing large datasets of image measurements, particularly when solving complex mathematical problems like inverse problems or optimization tasks. Traditional methods often require real-time computation, which can be resource-intensive and time-consuming. The system includes a discretization module that selects a discrete subset of values from a larger set of image measurements. This discretization reduces the computational complexity by limiting the range of values that need to be processed. A look-up table is then used, which contains precomputed solutions for the discrete set of values. This precomputation allows the system to retrieve solutions directly from the table rather than recalculating them, significantly speeding up the processing time. The look-up table is generated beforehand, storing results for anticipated input values, ensuring that the system can quickly access the necessary data during runtime. The system further includes a reconstruction module that uses the precomputed solutions from the look-up table to reconstruct the image or solve the underlying problem. This approach minimizes the need for on-the-fly calculations, making the system more efficient and scalable for real-time applications. The use of discretization and precomputed solutions ensures that the system can handle large datasets with reduced computational overhead, making it suitable for applications requiring fast and accurate image processing.
6. The image processing system of claim 1, wherein the look-up table comprises first corrected depth data for a first subset of the first set of measurements and second corrected depth data for a second subset of the first set of measurements.
The image processing system is designed to enhance depth data accuracy in imaging applications, particularly for systems that rely on depth measurements from sensors like LiDAR or structured light. The core challenge addressed is the presence of errors or distortions in raw depth measurements, which can degrade the quality of 3D reconstructions, object detection, or other depth-dependent applications. The system improves upon prior art by incorporating a look-up table that stores corrected depth data for subsets of the original measurements, allowing for efficient and accurate depth correction without requiring real-time computational adjustments. The look-up table contains two distinct sets of corrected depth data: the first set corresponds to a subset of the initial depth measurements, while the second set corresponds to another subset of the same measurements. This segmentation enables the system to apply different correction strategies or parameters to different parts of the depth data, improving overall accuracy. The corrected data can account for systematic errors, environmental factors, or sensor-specific artifacts, ensuring more reliable depth information for downstream processing. The system may also include additional components, such as calibration modules or interpolation algorithms, to further refine the depth data based on the look-up table entries. This approach reduces computational overhead while maintaining high precision in depth estimation.
7. The image processing system of claim 1, wherein the processor is further configured to undergo a calibration step.
The image processing system is designed to enhance image quality by reducing noise and improving clarity. The system includes a processor that performs image analysis and applies correction algorithms to raw image data. A key feature is the processor's ability to undergo a calibration step to optimize performance. During calibration, the processor adjusts parameters based on reference data or environmental conditions, ensuring accurate image processing. This step may involve analyzing test images, comparing results to known standards, or fine-tuning algorithms to account for sensor variations. The calibration process helps maintain consistent output quality across different operating conditions. The system may also include memory for storing calibration data and input/output interfaces for receiving raw images and outputting processed images. By incorporating calibration, the system adapts to changes in hardware or environmental factors, improving reliability and accuracy in image processing tasks.
9. The method of claim 8, further comprising generating a second set of measurements using the first set of measurements, wherein the second set of measurements is smaller than the first set of measurements.
The invention relates to data processing systems that reduce the size of measurement datasets while preserving essential information. The problem addressed is the computational and storage burden of large measurement datasets, which can slow down analysis and increase costs. The solution involves generating a second, smaller set of measurements derived from an initial larger set, ensuring the reduced dataset retains key characteristics of the original. The method begins with acquiring a first set of measurements, which may include raw sensor data, experimental readings, or other quantitative observations. These measurements are then processed to produce a second set of measurements that is smaller in size. The reduction process may involve techniques such as sampling, aggregation, or dimensionality reduction, ensuring that the smaller dataset remains representative of the original data. This allows for efficient storage and faster processing while maintaining accuracy for subsequent analysis or machine learning tasks. The approach is particularly useful in applications where real-time processing or resource constraints are critical, such as IoT devices, industrial monitoring, or scientific research.
10. The method of claim 9, further comprising performing dimensionality reduction on the first set of measurements to generate the second set of measurements.
This invention relates to data processing techniques for reducing the complexity of measurement datasets. The problem addressed is the computational and storage burden associated with high-dimensional data, which can hinder efficient analysis and real-time processing. The solution involves a method for transforming a first set of measurements into a second set of measurements with reduced dimensionality while preserving essential information. The method begins by obtaining a first set of measurements, which may include raw sensor data, feature vectors, or other high-dimensional datasets. These measurements are then processed through a dimensionality reduction technique, such as principal component analysis (PCA), autoencoders, or linear discriminant analysis (LDA), to generate a second set of measurements. The reduced-dimensionality output retains key patterns or features from the original data but in a more compact form, enabling faster processing, lower memory usage, and improved scalability. The dimensionality reduction step may involve mathematical transformations, feature selection, or machine learning-based compression. The technique is applicable across various domains, including signal processing, image analysis, and sensor networks, where reducing data complexity is critical for real-time applications or resource-constrained environments. The method ensures that the reduced dataset remains useful for subsequent analysis, such as classification, clustering, or anomaly detection, without significant loss of critical information.
11. The method of claim 9, further comprising discretizing the first set of measurements to generate the second set of measurements.
A method for processing sensor data involves discretizing a first set of measurements to generate a second set of measurements. The first set of measurements is obtained from one or more sensors, which may include inertial measurement units (IMUs), cameras, or other sensing devices. The discretization process converts continuous or high-resolution sensor data into a discrete or lower-resolution format, reducing data volume while preserving relevant information. This step is part of a broader system for analyzing sensor data, where the discretized measurements are used to estimate a state of a system, such as the position, orientation, or motion of an object. The method may also involve filtering or preprocessing the measurements before discretization to improve accuracy. The discretized data can then be transmitted, stored, or further processed for applications in robotics, navigation, or autonomous systems. The technique ensures efficient data handling while maintaining sufficient detail for accurate state estimation.
12. The method of claim 11, wherein discretizing the first set of measurements comprises selecting a discrete set of values from the first set of measurements, and wherein the look-up table comprises stored precomputed solutions for the discrete set of values.
This invention relates to a method for processing measurement data using a look-up table to improve computational efficiency. The method addresses the problem of high computational costs associated with real-time processing of large datasets, particularly in applications requiring rapid decision-making or control, such as industrial automation, sensor networks, or real-time analytics. The method involves discretizing a first set of measurements by selecting a discrete set of values from the measurements. These discrete values are then used to access a precomputed look-up table, which contains solutions for the discrete set of values. By using precomputed solutions, the method avoids the need for real-time calculations, significantly reducing processing time and resource usage. The look-up table is generated beforehand, storing results for anticipated measurement values, allowing for quick retrieval during operation. The method may also include generating the look-up table by computing solutions for the discrete set of values and storing them in the table. This preprocessing step ensures that the look-up table is populated with accurate and relevant data before runtime. The method further involves using the look-up table to retrieve precomputed solutions during runtime, enabling efficient and timely processing of the measurements. This approach is particularly useful in systems where real-time performance is critical, such as control systems, predictive maintenance, or real-time monitoring applications. By leveraging precomputed solutions, the method ensures fast and reliable processing while minimizing computational overhead.
13. The method of claim 8, wherein the look-up table comprises first stored corrected depth data for a first subset of the first set of measurements and second stored corrected depth data for a second subset of the first set of measurements.
This invention relates to depth sensing systems, particularly methods for correcting depth measurements using a look-up table. The problem addressed is the inaccuracy in depth measurements due to environmental factors, sensor noise, or other distortions, which can degrade the performance of applications like 3D imaging, augmented reality, or robotics. The method involves generating a look-up table that stores corrected depth data for subsets of raw depth measurements. The look-up table contains first corrected depth data for a first subset of measurements and second corrected depth data for a second subset of the same measurements. This allows for efficient retrieval and application of corrections tailored to specific measurement conditions. The look-up table may be precomputed based on calibration data or dynamically updated during operation to adapt to changing environments. The corrected depth data can account for factors like lens distortion, sensor misalignment, or environmental interference, improving the accuracy of depth perception in real-time applications. The method ensures that depth measurements are consistently accurate, enhancing the reliability of systems that depend on precise spatial information.
14. The method of claim 8, further comprising calibrating processor distance measurements.
A system and method for calibrating processor distance measurements in a computing environment. The technology addresses inaccuracies in distance measurements obtained by processors, which can lead to errors in applications requiring precise spatial data, such as augmented reality, robotics, or sensor networks. The method involves adjusting raw distance measurements from a processor to improve accuracy. This calibration process may include compensating for environmental factors, hardware limitations, or signal interference that distort measurements. The calibration may be performed using reference data, such as known distances or predefined correction factors, to refine the processor's output. The method ensures that distance measurements are reliable and consistent, enhancing the performance of applications dependent on spatial data. The calibration step may be applied dynamically during operation or as part of an initialization process, depending on the system requirements. By improving measurement accuracy, the method supports more precise positioning, navigation, and interaction in various technical domains.
16. The imaging apparatus of claim 15, further comprising means for processing the first set of measurements to determine a second set of measurements, wherein the second set of measurements is smaller than the first set of measurements.
This invention relates to imaging apparatus designed to reduce data volume while maintaining image quality. The apparatus captures a first set of measurements representing an image, which may include raw sensor data or intermediate processing results. A processing module then reduces this data by generating a second set of measurements that is smaller in size. The reduction process may involve techniques such as downsampling, compression, or feature extraction, ensuring that the essential information for image reconstruction is preserved. The apparatus may also include components for capturing the initial measurements, such as sensors or detectors, and additional processing units to enhance or analyze the reduced data. The goal is to optimize storage and transmission efficiency without significant loss of image fidelity, making it suitable for applications where bandwidth or memory constraints are critical, such as medical imaging, remote sensing, or high-resolution photography. The processing step ensures that the reduced data remains useful for subsequent analysis or display.
17. The imaging apparatus of claim 16, wherein processing the first set of measurements to determine the second set of measurements includes performing dimensionality reduction on the first set of measurements to generate the second set of measurements.
The invention relates to imaging apparatuses designed to enhance data processing efficiency, particularly in systems where high-dimensional measurement data must be analyzed or transmitted. The core problem addressed is the computational and bandwidth overhead associated with processing large volumes of raw measurement data, which can slow down real-time applications or require excessive storage and transmission resources. The imaging apparatus includes a sensor system configured to capture a first set of measurements from a target scene, where these measurements are typically high-dimensional and may include raw pixel data, depth information, or other sensor outputs. The apparatus further includes a processing unit that processes the first set of measurements to generate a second set of measurements, which is a reduced-dimensionality representation of the original data. This reduction is achieved through dimensionality reduction techniques, such as principal component analysis (PCA), autoencoders, or other mathematical transformations that retain essential features while discarding redundant or less significant information. The reduced-dimensionality second set of measurements is then used for further analysis, display, or transmission, significantly improving processing speed, reducing storage requirements, and lowering bandwidth demands. The apparatus may also include additional components, such as a display unit to visualize the processed data or a communication interface to transmit the reduced data to external systems. The dimensionality reduction step ensures that the apparatus can operate efficiently in resource-constrained environments while maintaining the integrity of critical information.
18. The imaging apparatus of claim 16, wherein processing the first set of measurements to determine the second set of measurements includes performing discretization on the first set of measurements to generate the second set of measurements.
This invention relates to imaging apparatuses designed to enhance image quality by processing measurement data. The apparatus addresses the problem of noise and inaccuracies in raw measurement data, which can degrade image resolution and clarity. The system captures a first set of measurements from an imaging sensor, which may include raw pixel data or other sensor readings. These measurements are then processed to generate a second set of measurements with improved accuracy and reduced noise. The processing step involves discretization, which converts the first set of measurements into a more refined or quantized form. Discretization may include techniques such as thresholding, binning, or other methods to reduce data variability while preserving essential image features. The processed measurements are then used to reconstruct or enhance the final image, improving signal-to-noise ratio and overall image quality. The apparatus may be applied in various imaging systems, including medical imaging, industrial inspection, or consumer electronics, where high-fidelity image data is critical. The discretization step ensures that the processed measurements are optimized for downstream image reconstruction algorithms, leading to clearer and more precise images.
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December 6, 2019
April 23, 2024
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